





from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		for param, grad_param in zip(model_a.parameters()       , model_b.parameters()     ):
				if not param.requires_grad:
						continue
				if not did_step:
						# Grads should not be in sync
						assert (
						    torch.allclose(param.grad       , grad_param.grad     ) is False
						), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
				else:
						# Grads should be in sync
						assert (
						    torch.allclose(param.grad       , grad_param.grad     ) is True
						), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple=True     ):
		model.train()
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   F.mse_loss(UpperCamelCase__       , target.to(output.device     )     )
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				List[Any]=False     ):
		set_seed(42     )
		SCREAMING_SNAKE_CASE__								=   RegressionModel()
		SCREAMING_SNAKE_CASE__								=   deepcopy(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		model.to(accelerator.device     )
		if sched:
				SCREAMING_SNAKE_CASE__								=   AdamW(params=model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   AdamW(params=ddp_model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
		# Make a copy of `model`
		if sched:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		# Test when on a single CPU or GPU that the context manager does nothing
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(UpperCamelCase__     ):
								step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				else:
						# Sync grads
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
				check_model_parameters(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				for param, ddp_param in zip(model.parameters()       , ddp_model.parameters()     ):
						if not param.requires_grad:
								continue
						assert torch.allclose(
						    param.grad       , ddp_param.grad     ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		# Test on distributed setup that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(UpperCamelCase__     ):
								step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				else:
						# Sync grads
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# DDP model and model should only be in sync when not (iteration % 2 == 0)
				for param, ddp_param in zip(model.parameters()       , ddp_model.parameters()     ):
						if not param.requires_grad:
								continue
						if iteration % 2 == 0:
								# Grads should not be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is False
								), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
						else:
								# Grads should be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is True
								), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int=False       , UpperCamelCase__:				Union[str, Any]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				with accelerator.accumulate(UpperCamelCase__     ):
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# DDP model and model should only be in sync when not (iteration % 2 == 0)
				for param, ddp_param in zip(model.parameters()       , ddp_model.parameters()     ):
						if not param.requires_grad:
								continue
						if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__     ) - 1):
								# Grads should be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is True
								), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
						else:
								# Grads should not be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is False
								), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple=False       , UpperCamelCase__:				List[str]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__       , UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				model.train()
				ddp_model.train()
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				opt.step()

				if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__     )):
						if split_batches:
								sched.step()
						else:
								for _ in range(accelerator.num_processes     ):
										sched.step()
				opt.zero_grad()
				# Perform gradient accumulation under wrapper
				with accelerator.accumulate(UpperCamelCase__     ):
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
						ddp_opt.step()
						ddp_sched.step()
						ddp_opt.zero_grad()

				# Learning rates should be the same
				assert (
				    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
				), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
				SCREAMING_SNAKE_CASE__								=   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__     ))
				if accelerator.num_processes > 1:
						check_model_parameters(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()

		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=96     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		assert accelerator.gradient_state.active_dataloader is None
		for iteration, _ in enumerate(UpperCamelCase__     ):
				assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
				if iteration < len(UpperCamelCase__     ) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
						if iteration == 1:
								for batch_num, _ in enumerate(UpperCamelCase__     ):
										assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
										if batch_num < len(UpperCamelCase__     ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
										else:
												assert accelerator.gradient_state.end_of_dataloader
				else:
						assert accelerator.gradient_state.end_of_dataloader
		assert accelerator.gradient_state.active_dataloader is None






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		SCREAMING_SNAKE_CASE__								=   accelerator.state
		if state.local_process_index == 0:
				print("""**Test `accumulate` gradient accumulation with dataloader break**"""     )
		test_dataloader_break()
		if state.distributed_type == DistributedType.NO:
				if state.local_process_index == 0:
						print("""**Test NOOP `no_sync` context manager**"""     )
				test_noop_sync(UpperCamelCase__     )
		if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
				if state.local_process_index == 0:
						print("""**Test Distributed `no_sync` context manager**"""     )
				test_distributed_sync(UpperCamelCase__     )
		if state.distributed_type == DistributedType.MULTI_GPU:
				for split_batch in [True, False]:
						for dispatch_batches in [True, False]:
								if state.local_process_index == 0:
										print(
										    """**Test `accumulate` gradient accumulation, """       , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''       , )
								test_gradient_accumulation(UpperCamelCase__       , UpperCamelCase__     )

    # Currently will break on torch 2.0 +, need to investigate why
		if is_torch_version("""<"""       , """2.0"""     ) or state.distributed_type == DistributedType.NO:
				if state.local_process_index == 0:
						print(
						    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """       , """`split_batches=False`, `dispatch_batches=False`**"""       , )
				test_gradient_accumulation_with_opt_and_scheduler()
				if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
								for dispatch_batches in [True, False]:
										if not split_batch and not dispatch_batches:
												continue
										if state.local_process_index == 0:
												print(
												    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """       , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''       , )
										test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()




'''simple docstring'''






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class 				a__(   lowerCamelCase__						,					lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       IFPipeline
    lowercase__					=       TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
    lowercase__					=       TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase__					=       PipelineTesterMixin.required_optional_params - {"""latents"""}







    def      lowercase_			( self					:    List[Any]      ):
      return self._get_dummy_components()







    def      lowercase_			( self					:    int		,     __snake_case					:    List[str]		,     __snake_case					:    List[Any]=0      ):
      if str(__snake_case      ).startswith('mps'      ):
        a		:   List[Any]	  =	torch.manual_seed(__snake_case      )
      else:
        a		:   str	  =	torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )

      a		:   Dict	  =	{
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs







    def      lowercase_			( self					:    Optional[Any]      ):
      self._test_save_load_optional_components()







    @unittest.skipIf(torch_device != 'cuda'		,     reason='float16 requires CUDA'      )
    def      lowercase_			( self					:    Union[str, Any]      ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1e-1      )







    def      lowercase_			( self					:    Tuple      ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )







    def      lowercase_			( self					:    str      ):
      self._test_save_load_local()







    def      lowercase_			( self					:    Any      ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2		,     )







    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()		,     reason='XFormers attention is only available with CUDA and `xformers` installed'		,     )
    def      lowercase_			( self					:    List[str]      ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )

@slow
@require_torch_gpu
class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    Optional[Any]      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def      lowercase_			( self					:    Tuple      ):
      # if

      a		:   List[Any]	  =	IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'		,     variant='fp16'		,     torch_dtype=torch.floataa      )

      a		:   Optional[int]	  =	IFSuperResolutionPipeline.from_pretrained(
          'DeepFloyd/IF-II-L-v1.0'		,     variant='fp16'		,     torch_dtype=torch.floataa		,     text_encoder=__snake_case		,     tokenizer=__snake_case      )

      # pre compute text embeddings and remove T5 to save memory

      pipe_a.text_encoder.to('cuda'      )

      a  ,      a		:   Optional[Any]	  =	pipe_a.encode_prompt('anime turtle'		,     device='cuda'      )

      del pipe_a.tokenizer
      del pipe_a.text_encoder
      gc.collect()

      a		:   List[Any]	  =	None
      a		:   Dict	  =	None

      pipe_a.enable_model_cpu_offload()
      pipe_a.enable_model_cpu_offload()

      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

      self._test_if(__snake_case		,     __snake_case		,     __snake_case		,     __snake_case      )

      pipe_a.remove_all_hooks()
      pipe_a.remove_all_hooks()

      # img2img

      a		:   List[str]	  =	IFImgaImgPipeline(**pipe_a.components      )
      a		:   int	  =	IFImgaImgSuperResolutionPipeline(**pipe_a.components      )

      pipe_a.enable_model_cpu_offload()
      pipe_a.enable_model_cpu_offload()

      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

      self._test_if_imgaimg(__snake_case		,     __snake_case		,     __snake_case		,     __snake_case      )

      pipe_a.remove_all_hooks()
      pipe_a.remove_all_hooks()

      # inpainting

      a		:   Tuple	  =	IFInpaintingPipeline(**pipe_a.components      )
      a		:   int	  =	IFInpaintingSuperResolutionPipeline(**pipe_a.components      )

      pipe_a.enable_model_cpu_offload()
      pipe_a.enable_model_cpu_offload()

      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

      self._test_if_inpainting(__snake_case		,     __snake_case		,     __snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Optional[int]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Tuple		,     __snake_case					:    List[str]      ):
      # pipeline 1

      _start_torch_memory_measurement()

      a		:   int	  =	torch.Generator(device='cpu'      ).manual_seed(0      )
      a		:   int	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     num_inference_steps=2		,     generator=__snake_case		,     output_type='np'		,     )

      a		:   Dict	  =	output.images[0]

      assert image.shape == (64, 64, 3)

      a		:   Any	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 13 * 10**9

      a		:   Optional[int]	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )

      # pipeline 2

      _start_torch_memory_measurement()

      a		:   List[str]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   List[Any]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )

      a		:   Any	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     generator=__snake_case		,     num_inference_steps=2		,     output_type='np'		,     )

      a		:   Tuple	  =	output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      a		:   Optional[Any]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9

      a		:   Tuple	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    str		,     __snake_case					:    int		,     __snake_case					:    Any		,     __snake_case					:    Any      ):
      # pipeline 1

      _start_torch_memory_measurement()

      a		:   Optional[Any]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )

      a		:   Optional[int]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   Any	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     num_inference_steps=2		,     generator=__snake_case		,     output_type='np'		,     )

      a		:   List[Any]	  =	output.images[0]

      assert image.shape == (64, 64, 3)

      a		:   Optional[Any]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 10 * 10**9

      a		:   Tuple	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )

      # pipeline 2

      _start_torch_memory_measurement()

      a		:   Optional[int]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   str	  =	floats_tensor((1, 3, 2_56, 2_56)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Any	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )

      a		:   Dict	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     original_image=__snake_case		,     generator=__snake_case		,     num_inference_steps=2		,     output_type='np'		,     )

      a		:   Optional[Any]	  =	output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      a		:   Any	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9

      a		:   Dict	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    Tuple		,     __snake_case					:    Any		,     __snake_case					:    str		,     __snake_case					:    int      ):
      # pipeline 1

      _start_torch_memory_measurement()

      a		:   Tuple	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Union[str, Any]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(1      )      ).to(__snake_case      )

      a		:   Union[str, Any]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )
      a		:   str	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     mask_image=__snake_case		,     num_inference_steps=2		,     generator=__snake_case		,     output_type='np'		,     )

      a		:   List[Any]	  =	output.images[0]

      assert image.shape == (64, 64, 3)

      a		:   List[str]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 10 * 10**9

      a		:   Optional[int]	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )

      # pipeline 2

      _start_torch_memory_measurement()

      a		:   Union[str, Any]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   Optional[int]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Tuple	  =	floats_tensor((1, 3, 2_56, 2_56)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Optional[int]	  =	floats_tensor((1, 3, 2_56, 2_56)		,     rng=random.Random(1      )      ).to(__snake_case      )

      a		:   int	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     mask_image=__snake_case		,     original_image=__snake_case		,     generator=__snake_case		,     num_inference_steps=2		,     output_type='np'		,     )

      a		:   List[str]	  =	output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      a		:   Union[str, Any]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9

      a		:   Any	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )
def  lowerCamelCase__			(		):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()